measure_cac_script.jl

### A Pluto.jl notebook ###
# v0.19.40

using Markdown
using InteractiveUtils

# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).
macro bind(def, element)
    quote
        local iv = try Base.loaded_modules[Base.PkgId(Base.UUID("6e696c72-6542-2067-7265-42206c756150"), "AbstractPlutoDingetjes")].Bonds.initial_value catch; b -> missing; end
        local el = $(esc(element))
        global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : iv(el)
        el
    end
end

# ╔═╡ 7879e86f-e900-45de-a7ba-5e768760d1b1
using DataFrames: DataFrame, rename!, nrow

# ╔═╡ c089c448-09c1-459c-8c88-07b021e1b6f5
using CSV: write

# ╔═╡ 7ede2cbc-c82a-40c4-86be-ac7c61c14f03
using StaticArrays: SVector

# ╔═╡ 8d24da97-a7ab-4fb8-b37b-c48c6c3d3f70
using IterTools: product

# ╔═╡ b8c49cef-c730-464b-b17c-005afdf0fdbe
using LinearAlgebra: normalize, dot

# ╔═╡ aa1f5386-f4f3-4bf6-a19d-41814fdd19f9
using CairoMakie: Figure, Axis, heatmap!, heatmap, scatter!, axislegend, hist!

# ╔═╡ 0cd17411-918f-4f02-b4a9-ffaa6de64bc8
using DICOM: dcmdir_parse

# ╔═╡ a7e5315b-adee-4168-a321-755a49016433
using PlutoUI: TableOfContents, Slider, TextField, confirm, combine

# ╔═╡ c4b10ab5-ef81-4617-be00-6c0e836dcdd8
using StatsBase: countmap, quantile

# ╔═╡ d39fe463-c2b7-4297-918a-490a31b4329e
using ImageMorphology: component_centroids, label_components, component_boxes, dilate

# ╔═╡ 5102b829-2ed0-430b-a942-72d96e21c197
using Statistics: mean, norm, std

# ╔═╡ bd259589-0cc3-40e2-a8e4-c3af710bf65a
using CalciumScoring: score, VolumeFraction, Agatston

# ╔═╡ 41d34cdb-af4c-4c59-9ae8-e020b72b6964
using ImageCore: channelview

# ╔═╡ 29fd1197-53c7-4660-849e-f1e684acd8d1
using DataInterpolations: QuadraticInterpolation

# ╔═╡ 10872202-4a25-4a8b-ab1d-35c4ece5a2a2
using OrderedCollections: OrderedDict

# ╔═╡ beda1edb-4fea-40d1-8bbc-c49953952698
md"""
# Set Up
"""

# ╔═╡ a214f633-3699-4fe6-bca4-02de420d3c1d
md"""
## Environment & Imports
"""

# ╔═╡ 4aff31dc-7fd7-4d89-9a31-26cd1133eeb5
TableOfContents()

# ╔═╡ f97c1ff0-36ae-4fcf-b7db-065c90ea13cf
md"""
## Helper Functions
"""

# ╔═╡ ae78dad9-4246-4c8b-87d3-d0ddc6f9316d
md"""
#### DICOM Utils
"""

# ╔═╡ a4aec909-8214-4615-8782-f4968c532742
function load_dcm_array(dcm_data)
    return array = cat(
        [dcm_data[i][(0x7fe0, 0x0010)] for i in eachindex(dcm_data)]...; dims=3
    )
end

# ╔═╡ b1ad4ce5-c28d-48bb-b249-3760a6acbaec
function get_pixel_size(header)
	head = copy(header)
	pixel_size = 
		try
			pixel_size = (head[(0x0028, 0x0030)])
            push!(pixel_size, head[(0x0018, 0x0050)])
		catch
			FOV = (head[(0x0018, 0x1100)])
			matrix_size = head[(0x0028, 0x0010)]
		
			pixel_size = FOV / matrix_size
            push!(pixel_size, head[(0x0018, 0x0050)])
		end
	return pixel_size
end

# ╔═╡ a95486b1-8cb3-4f03-b761-595bc24bd70c
md"""
#### Active Contours
"""

# ╔═╡ 5419e1ba-1709-4c4b-8a3d-edaef052cbcd
function initial_level_set(shape::Tuple{Int64, Int64})
    x₀ = reshape(collect(0:shape[begin]-1), shape[begin], 1)
    y₀ = reshape(collect(0:shape[begin+1]-1), 1, shape[begin+1])
    𝚽₀ = @. sin(pi / 5 * x₀) * sin(pi / 5 * y₀)
end

# ╔═╡ 42c07efe-f575-40a6-a65b-57b521061c5e
function initial_level_set(shape::Tuple{Int64, Int64, Int64})
    x₀ = reshape(collect(0:shape[begin]-1), shape[begin], 1, 1)
    y₀ = reshape(collect(0:shape[begin+1]-1), 1, shape[begin+1], 1)
    z₀ = reshape(collect(0:shape[begin+2]-1), 1, 1, shape[begin+2])
    𝚽₀ = @. sin(pi / 5 * x₀) * sin(pi / 5 * y₀) * sin(pi / 5 * z₀)
end

# ╔═╡ b7d72bb0-4ed8-4d78-bf47-4f333a2daee7
function calculate_averages(
	img::AbstractArray{T, N},
	H𝚽::AbstractArray{S, N},
	area::Int64, ∫u₀::Float64) where {T<:Real, S<:Bool, N}
    ∫u₀H𝚽 = 0
    ∫H𝚽 = 0
    @inbounds for i in eachindex(img)
        if H𝚽[i]
            ∫u₀H𝚽 += img[i]
            ∫H𝚽 += 1
        end
    end
    ∫H𝚽ⁱ = area - ∫H𝚽
    ∫u₀H𝚽ⁱ = ∫u₀ - ∫u₀H𝚽
    c₁ = ∫u₀H𝚽 / max(1, ∫H𝚽)
    c₂ = ∫u₀H𝚽ⁱ / max(1, ∫H𝚽ⁱ)

    return c₁, c₂
end

# ╔═╡ e5477a2e-9f40-41f3-888e-0c301302de64
function chan_vese(img;
                   μ::Real=0.25,
                   λ₁::Real=1.0,
                   λ₂::Real=1.0,
                   tol::Real=1e-3,
                   max_iter::Int=500,
                   Δt::Real=0.5,
                   normalize::Bool=false,
                   init_level_set=initial_level_set(size(img)))
	# Signs used in the codes and comments mainly follow paper[3] in the References.
	axes(img) == axes(init_level_set) || throw(ArgumentError("axes of input image and init_level_set should be equal. Instead they are $(axes(img)) and $(axes(init_level_set))."))
	img = Float64.(channelview(img))
	N = ndims(img)
	iter = 0
	h = 1.0
	del = tol + 1
	if normalize
		img .= img .- minimum(img)
		if maximum(img) != 0
			img .= img ./ maximum(img)
		end
	end
	
	# Precalculation of some constants which helps simplify some integration
	area = length(img)   # area = ∫H𝚽 + ∫H𝚽ⁱ
	∫u₀ = sum(img)       # ∫u₀ = ∫u₀H𝚽 + ∫u₀H𝚽ⁱ
	
	# Initialize the level set
	𝚽ⁿ = init_level_set
	
	# Preallocation and initializtion
	H𝚽 = trues(size(img)...)
	𝚽ⁿ⁺¹ = similar(𝚽ⁿ)
	
	Δ = ntuple(d -> CartesianIndex(ntuple(i -> i == d ? 1 : 0, N)), N)
	idx_first = first(CartesianIndices(𝚽ⁿ))
	idx_last = last(CartesianIndices(𝚽ⁿ))
	
	while (del > tol) & (iter < max_iter)
		ϵ = 1e-8
		diff = 0
	
		# Calculate the average intensities
		@. H𝚽 = 𝚽ⁿ > 0 # Heaviside function
		c₁, c₂ = calculate_averages(img, H𝚽, area, ∫u₀) # Compute c₁(𝚽ⁿ), c₂(𝚽ⁿ)
	
		# Calculate the variation of level set 𝚽ⁿ
		@inbounds @simd for idx in CartesianIndices(𝚽ⁿ)
			𝚽₀  = 𝚽ⁿ[idx] # 𝚽ⁿ(x, y)
			u₀ = img[idx]  # u₀(x, y)
			𝚽₊ = broadcast(i->𝚽ⁿ[i], ntuple(d -> idx[d] != idx_last[d]  ? idx + Δ[d] : idx, N))
			𝚽₋ = broadcast(i->𝚽ⁿ[i], ntuple(d -> idx[d] != idx_first[d] ? idx - Δ[d] : idx, N))
	
			# Solve the PDE of equation 9 in paper[3]
			center_diff = ntuple(d -> (𝚽₊[d] - 𝚽₋[d])^2 / 4., N)
			sum_center_diff = sum(center_diff)
			C₊ = ntuple(d -> 1. / sqrt(ϵ + (𝚽₊[d] - 𝚽₀)^2 + sum_center_diff - center_diff[d]), N)
			C₋ = ntuple(d -> 1. / sqrt(ϵ + (𝚽₋[d] - 𝚽₀)^2 + sum_center_diff - center_diff[d]), N)
	
			K = sum(𝚽₊ .* C₊) + sum(𝚽₋ .* C₋)
			δₕ = h / (h^2 + 𝚽₀^2) # Regularised Dirac function
			difference_from_average = - λ₁ * (u₀ - c₁) ^ 2 + λ₂ * (u₀ - c₂) ^ 2
	
			𝚽ⁿ⁺¹[idx] = 𝚽 = (𝚽₀ + Δt * δₕ * (μ * K + difference_from_average)) / (1. + μ * Δt * δₕ * (sum(C₊) + sum(C₋)))
			diff += (𝚽 - 𝚽₀)^2
		end
	
		del = sqrt(diff / area)
	
		# Reinitializing the level set is not strictly necessary, so this part of code is commented.
		# If you wants to use the reinitialization, just uncommented codes following.
		# Function `reinitialize!` is already prepared.
	
		# reinitialize!(𝚽ⁿ⁺¹, 𝚽ⁿ, Δt, h) # Reinitialize 𝚽 to be the signed distance function to its zero level set
	
		𝚽ⁿ .= 𝚽ⁿ⁺¹
	
		iter += 1
	end
	
	return 𝚽ⁿ .> 0
end

# ╔═╡ 26ff0532-dbf6-485d-a83f-199d0f70b0e6
md"""
#### Masks
"""

# ╔═╡ 0a61db85-1f29-4b90-a4da-651f15b0f38b
function create_mask(array, mask)
    @assert size(array) == size(mask)
    idxs = findall(x -> x == true, mask)
    overlayed_mask = zeros(size(array))
    for idx in idxs
        overlayed_mask[idx] = array[idx]
    end
    return overlayed_mask
end

# ╔═╡ aae0aaec-8268-4765-a451-0bceb31dd6e2
function calculate_thresholds(kV, mA)
	if kV == 80
		mA_arr = [10, 20, 40, 100, 150, 250]
		threshold_low_arr = [100, 80, 50, 20, 10, 0]
		threshold_high_arr = [300, 255, 180, 90, 65, 40]
    elseif kV == 100
		mA_arr = [10, 20, 40, 100, 150, 250]
		threshold_low_arr = [70, 55, 35, 35, 29, 25]
		threshold_high_arr = [230, 150, 90, 88, 83, 80]
    elseif kV == 120
		mA_arr = [10, 15, 20, 25, 30, 40, 50, 100, 150, 250]
		threshold_low_arr = [40, 40, 39, 39, 39, 39, 39, 35, 23, 22]
		threshold_high_arr = [150, 110, 105, 100, 100, 95, 90, 90, 88, 85]
    end

	threshold_low_interp = QuadraticInterpolation(threshold_low_arr, mA_arr; extrapolate = true)
	threshold_high_interp = QuadraticInterpolation(threshold_high_arr, mA_arr; extrapolate = true)

	threshold_low = threshold_low_interp(mA)
	threshold_high = threshold_high_interp(mA)
    return threshold_low, threshold_high
end

# ╔═╡ 172d043b-c600-43e6-a75d-524bc9dd4955
function threshold_low_high(dcm_arr, kV, mA)
    threshold_low, threshold_high = calculate_thresholds(kV, mA)
	thresholded_mask_low = dcm_arr .> threshold_low
    thresholded_mask_high = dcm_arr .< threshold_high
    masked_thresholded = thresholded_mask_high .& thresholded_mask_low

    return masked_thresholded
end

# ╔═╡ 18e0ba73-e0f5-42b5-a915-d9287bd9a006
md"""
#### Orthanc Tools
"""

# ╔═╡ bf3d544f-9ab8-430a-b7cb-5da5c7381d52
import HTTP

# ╔═╡ 2861ed2a-adfe-4375-a2cb-0d090f8f1e02
import JSON

# ╔═╡ 89354634-0bec-4d40-a71a-d48f3d9727a1
function get_all_studies(ip_address::String="localhost"; show_warnings=false)
	url_studies = HTTP.URI(scheme="http", host=ip_address, port="8042", path="/studies")
    studies = JSON.parse(String(HTTP.get(url_studies).body))

    studies_dict = OrderedDict{String,Vector}()
    for study in studies
        s = JSON.parse(String(HTTP.get(string(url_studies, "/", study)).body))
        try
            accession_num = s["MainDicomTags"]["AccessionNumber"]
            if !haskey(studies_dict, accession_num)
                push!(studies_dict, accession_num => [study])
            else
                push!(studies_dict[accession_num], study)
            end
        catch
			if show_warnings
            	@warn "No accession number for $study"
			end
        end
    end

    return studies_dict
end

# ╔═╡ b139733c-638e-40e5-877e-5b2033838f18
function get_all_series(
    studies_dict::OrderedDict,
    accession_num::String,
    ip_address::String="localhost")

	url_study = HTTP.URI(scheme="http", host=ip_address, port="8042", path="/studies/$(studies_dict[accession_num]...)")

    series = JSON.parse(String(HTTP.get(url_study).body))
    series_dict = OrderedDict{String,Vector}()
    for ser in series["Series"]
		url_series = HTTP.URI(
			scheme="http", host=ip_address, port="8042", path=string("/series/", ser)
		)
        s = JSON.parse(String(HTTP.get(url_series).body))
        try
            series_num = s["MainDicomTags"]["SeriesNumber"]
            if !haskey(series_dict, series_num)
                push!(series_dict, series_num => [ser])
            else
                push!(series_dict[series_num], ser)
            end
        catch
            @warn "No series number for $ser"
        end
    end

    return series_dict
end

# ╔═╡ a75ef71d-c5de-4614-bb76-09355919d783
function get_all_instances(
    series_dict::OrderedDict,
    series_num::String,
    ip_address::String="localhost")

    url = HTTP.URI(scheme="http", host=ip_address, port="8042")
    instances_dict = OrderedDict{String,Vector}()
    for ser in series_dict[series_num]
		url_series = HTTP.URI(
			scheme="http", host=ip_address, port="8042", path=string("/series/", ser)
		)
        series = JSON.parse(String(HTTP.get(url_series).body))
        instances = series["Instances"]
        if !haskey(instances_dict, series_num)
            push!(instances_dict, series_num => [instances])
        else
            push!(instances_dict[series_num], instances)
        end
    end
    return instances_dict
end

# ╔═╡ 08774908-cb99-4673-8470-7881301da059
function download_instances(
	instances_dict::OrderedDict,
	instance_num::Number,
	output_directory::String,
	ip_address::String="localhost")

	for (key, value) in instances_dict
		for i in value[instance_num]
			url_instance = string("http://", ip_address, ":8042", string("/instances/", i))
			instance = JSON.parse(String(HTTP.get(url_instance).body))
			idx = instance["IndexInSeries"]
			download(string(url_instance, "/", "file"), joinpath(output_directory, "$(idx).dcm"))
		end
	end
	@info "Files located at $(output_directory)"
end

# ╔═╡ 69c5661c-77c0-4b6b-b0bc-3cf1512b7c60
function process_instances(
	instances_dicts, series_num_vec, output_dir, instance_number, ip_address
)
    if !isdir(output_dir)
        mkpath(output_dir)
    end

    output_paths = String[]

	for (idx, dict) in enumerate(instances_dicts)
		_series = collect(keys(dict))[1]
		
	    path = joinpath(output_dir, string(series_num_vec[idx]))
	    if !isdir(path)
	        mkpath(path)
	    end
		
	    download_instances(instances_dicts[idx], instance_number, path, ip_address)
	
	    output_path = joinpath(output_dir, string(_series))
		push!(output_paths, output_path)
	end

    return output_paths
end

# ╔═╡ da41d9b3-e124-4ef8-81e0-d3e5eea52337
md"""
#### Mask Heart Function
"""

# ╔═╡ 17f9ba72-aa80-4ff5-9de2-83512b6c49ce
function erode_mask(img, num_erosions)
	new_img = img
	i = 0
	while i < num_erosions
		new_img = erode(new_img)
		i += 1
	end
	return new_img
end

# ╔═╡ 7b84a208-0a59-4aeb-9506-df5eff303305
function create_circle_mask(img, centroids, radius)
    # initialize mask with all zeros
    mask = zeros(size(img))

    # define the center of the circle
    x0, y0 = centroids[1], centroids[2]

    # set all pixels inside the circle to 1 and all pixels outside the circle to 0
    for x in axes(img, 1), y in axes(img, 2)
        if ((x - x0)^2 + (y - y0)^2) <= radius^2
            mask[x, y] = 1
        else
            mask[x, y] = 0
        end
    end
    return Bool.(mask)
end

# ╔═╡ cdb712cf-12ee-4e9d-9d04-16f4af14bbd8
function centroids_from_mask(mask)
	cc_labels = label_components(mask)
	largest_connected_component, _ = sort(collect(pairs(countmap(cc_labels[cc_labels .!= 0]))), by=x->x[2], rev=true)
	largest_connected_indices = findall(cc_labels .== largest_connected_component[1])

	new_mask = zeros(size(mask))
	for i in largest_connected_indices
		new_mask[i] = 1
	end
	centroids = Int.(round.(component_centroids(label_components(new_mask))[end]))
end

# ╔═╡ 3f695cba-2572-4a50-b76a-80fec1056692
function create_initial_level_set(dcm_arr)
	half_x, half_y = size(dcm_arr, 1) ÷ 2, size(dcm_arr, 2) ÷ 2
	init_circle = create_circle_mask(dcm_arr[:, :, 3], (half_x, half_y), 140)
	
	init_mask = BitArray(undef, size(dcm_arr))
	for z in axes(dcm_arr, 3)
		init_mask[:, :, z] = init_circle
	end
	init_mask = init_mask .* initial_level_set(size(init_mask))

	return init_mask
end

# ╔═╡ c33aaaa0-6a87-41b4-86ed-669016382b6e
md"""
#### Inserts
"""

# ╔═╡ c9648ed4-44a2-41c0-b019-e8d68e2a7a6a
function get_insert_centers(dcm, threshold)
	dcm_slice_thresh = dcm .> threshold
	
	# Use connected component labeling to identify and label all connected components
	cc_labels = label_components(dcm_slice_thresh)

	# Use the countmap function to count the number of occurrences of each value in the array, excluding 0
	counts = countmap(cc_labels[cc_labels .!= 0])
	
	# Find the value with the most occurrences
	most_common_value_a, most_common_value_b = sort(collect(pairs(counts)), by=x->x[2], rev=true)

	# Find the indices of the most common value in the original array
	most_common_indices_a = findall(cc_labels .== most_common_value_a[1])

	# Create boolean array from new cartesian indices
	bool_arr_a = zeros(size(dcm_slice_thresh))
	for i in most_common_indices_a
		bool_arr_a[i] = 1
	end
	centroids_a = Int.(round.(component_centroids(label_components(bool_arr_a))[end]))
	box_a = component_boxes(label_components(bool_arr_a))

	# Find the indices of the most common value in the original array
	most_common_indices_b = findall(cc_labels .== most_common_value_b[1])

	# Create boolean array from new cartesian indices
	bool_arr_b = zeros(size(dcm_slice_thresh))
	for i in most_common_indices_b
		bool_arr_b[i] = 1
	end
	centroids_b = Int.(round.(component_centroids(label_components(bool_arr_b))[end]))

	# centers_a, centers_b = [centroids_a..., z], [centroids_b..., z]
	centers_a, centers_b = centroids_a, centroids_b
	return centers_a, centers_b
	
end

# ╔═╡ e62f58f7-141f-4c1a-94e0-561d95eec28b
# Modify the in_cylinder function to accept Static Vectors
function _in_cylinder(pt::SVector{3, Int}, pt1::SVector{3, Float64}, pt2::SVector{3, Float64}, radius)
    v = pt2 - pt1
    w = pt - pt1

    # Compute the dot product
    c1 = dot(w, v)
    if c1 <= 0
        return norm(w) <= radius
    end

    c2 = dot(v, v)
    if c2 <= c1
        return norm(pt - pt2) <= radius
    end

    # Compute the perpendicular distance
    b = c1 / c2
    pb = pt1 + b * v
    return norm(pt - pb) <= radius
end

# ╔═╡ 0b72af17-61fc-4b86-a338-8f86d32058fe
function create_cylinder(array, pt1, pt2, radius, offset)
    # Convert the points to static arrays
    pt1 = SVector{3, Float64}(pt1)
    pt2 = SVector{3, Float64}(pt2)

    # Compute the unit vector in the direction from pt1 to pt2
    direction = normalize(pt2 - pt1)

    # Adjust the endpoints of the cylinder by the offset
    pt1 = pt1 - offset * direction
    pt2 = pt2 + offset * direction

    # Initialize the 3D array
    cylinder = zeros(Int, size(array)...)
    # Iterate over the 3D array
    for k in axes(cylinder, 3)
        for j in axes(cylinder, 2)
            for i in axes(cylinder, 1)
                # Create a static vector for the current point
                pt = SVector{3, Int}(i, j, k)

                # Check if the current point is inside the cylinder
                if _in_cylinder(pt, pt1, pt2, radius)
                    cylinder[i, j, k] = 1
                end
            end
        end
    end
    return Bool.(cylinder)
end

# ╔═╡ d61f577c-531a-4f16-a806-5bcfc64a94c2
function remove_outliers(vector)
    Q1 = quantile(vector, 0.25)
    Q3 = quantile(vector, 0.75)
    IQR = Q3 - Q1
    lower_bound = Q1 - 1.5 * IQR
    return [x for x in vector if x > lower_bound]
end

# ╔═╡ da4739f0-4857-4b12-a5a9-bab91c543694
md"""
## Docs
"""

# ╔═╡ d6f176df-b323-4d74-b609-09a50b82a15d
md"""
!!! info ""
	See `README.md` in the root directory for more info on the scans

	| Accession Number | Scan Name | Series (80 kV) | Series (100 kV) | Series (120 kV) |
	| ---------------- | --------- | -------------- | --------------- | --------------- |
	| 3082             | C_0bpm    | 2-11           | 12-21           | 22-31           |
	| 3083             | F_0bpm    | 2-11           | 12-21           | 22-31           |
	| 3087             | E_0bpm    | 2-11           | 12-21           | 22-31           |
	| 3095             | D_0bpm    | 2-11           | 12-21           | 22-31           |
"""

# ╔═╡ 4f0f5767-5c26-4ae4-a28c-20ca9ed986ee
md"""
# Download From Orthanc
"""

# ╔═╡ 96c15de9-3100-4d4d-b2e0-1380cf0ec31f
md"""
## Get Studies
Insert the IP address associated with the Orthanc server into the input box below and then click "Submit". When the code is finished, you can inspect the files by clicking on the dictionary.
"""

# ╔═╡ 1c611a7b-a89c-4bd3-99cc-296a4ccf43a5
@bind ip_address confirm(TextField(default="128.200.49.26"))

# ╔═╡ af303018-55b9-4330-8e8d-a3e7d1d98697
studies_dict = get_all_studies(ip_address)

# ╔═╡ 6dae0032-6a60-4421-a68e-a94c44416e6e
md"""
## Get Series
Insert the accession number into the input box above and click "Submit". When the code is finished, you can inspect the files by clicking on the dictionary.
"""

# ╔═╡ 6030b8c8-d10b-442b-875c-c9e8f5bd016a
md"""
## Get Instance(s)
You can insert the series number of interest into the input box above and then click "Submit". When the code is finished, you can inspect the files by clicking on the dictionary.
"""

# ╔═╡ b7807c48-6abe-416e-bbd9-5922e9044f33
md"""
# Download DICOM Instance(s)
Type the folder path above, where you want the DICOM files to be saved (or use a temporary directory via `mktempdir()`) in the code cell below. Then type in the instance number that you want to download and click "Submit".
"""

# ╔═╡ 4beb3247-28f0-4b30-b925-0061253ee304
output_dir_temp = mktempdir() # r

# ╔═╡ 87bc30a2-7b7e-4877-8618-aac5b44f88db
function download_info(acc, ser, inst, save_folder_path)
	
	return combine() do Child
		
		inputs = [
			md""" $(acc): $(
				Child(TextField(default="3082"))
			)""",
			md""" $(ser): $(
				Child(TextField(default="2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31"))
			)""",
			md""" $(inst): $(
				Child(TextField(default="1"))
			)""",
			md""" $(save_folder_path): $(
				Child(TextField(default=output_dir_temp)))
			)"""
		]
		
		md"""
		#### Scan Details
		Input the relevant DICOM information to download the appropriate scans
		$(inputs)
		"""
	end
end

# ╔═╡ bb89a69b-35fc-4b42-a36c-9ac7640476c8
@bind details confirm(download_info("Accession Number", "Series Number(s)", "Instance Number", "Output Directory"))

# ╔═╡ e0e10a0c-464b-4185-bff7-d0a4e577cae1
accession_number, series_num, instance_num, output_dir = details

# ╔═╡ a23a7779-6b79-444c-a172-33afdf5fa204
series_dict = get_all_series(studies_dict, accession_number, ip_address)

# ╔═╡ 850df889-4876-4018-bf79-9de790d10091
series_num_vec = parse.(Int, split(series_num, ","))

# ╔═╡ 061edd0d-31a1-4f9a-842e-9b3c113d6e4b
begin
	instances_dicts = []
	for i in series_num_vec
		instances_dict = get_all_instances(series_dict, string(i), ip_address)
		push!(instances_dicts, instances_dict)
	end
end

# ╔═╡ 1cbc8875-8b23-4660-8eb6-543f31436c73
instances_dicts

# ╔═╡ 986a7b15-a441-412f-88a7-25f9648cfcbc
instance_number = parse(Int64, instance_num)

# ╔═╡ d60685cd-846f-4591-9908-2f2275a193d0
instances_dicts

# ╔═╡ 08e918cd-0032-4f8e-be16-84f2b19d8291
md"""
# Run Script
"""

# ╔═╡ b22040f6-5726-4551-af1d-b65ae876e83c
begin
	results_df = DataFrame(
		insert_name = String[],
		beats_per_minute = String[],
		kV = Float64[],
		mA = Float64[],
		ctdi = Float64[],
		gt_mass = Float64[],
		vf_mass = Float64[],
		vf_mass_bkg_mean = Float64[],
		vf_mass_bkg_std = Float64[],
		agatston_mass = Float64[],
		agatston_mass_bkg_mean = Float64[],
		agatston_mass_bkg_std = Float64[]
	)

	local insert_name
	local beats_per_minute
	for (i, dicts) in enumerate(instances_dicts)
		@info i
		output_paths = process_instances([dicts], series_num_vec[i], output_dir, instance_number, ip_address)
		
		output_path = output_paths[end]
	
		# Load DICOMs
		dcms = dcmdir_parse(output_path)
		dcm_arr = load_dcm_array(dcms)
	
		header = dcms[1].meta
		scan_name = header[(0x0010, 0x0020)]
		insert_name = split(scan_name, "_")[1]
		beats_per_minute = split(scan_name, "_")[2]
		
		mA = header[(0x0018, 0x1151)]
		kV = header[(0x0018, 0x0060)]
		ctdi = header[(0x0018, 0x9345)] # mGy
		x_space, y_space = header[(0x0028, 0x0030)]
		slice_thickness = header[(0x0018, 0x0050)]
		pixel_size = [x_space, y_space, slice_thickness]
	
		# # Mask Heart
		# mask_thresholded = threshold_low_high(dcm_arr, kV, mA)
		# centroids = centroids_from_mask(mask_thresholded)

		centroids = div.(size(dcm_arr), 2)
		heart_rad = 95
		heart_mask = create_circle_mask(dcm_arr[:, :, 3], centroids, heart_rad)
	
		## Extract density
		if insert_name == "A" || insert_name == "B" || insert_name == "C"
			gt_density = 0.050  # mg/mm^3
		elseif insert_name == "D" || insert_name == "E" || insert_name == "F"
			gt_density = 0.100  # mg/mm^3
		end

		## Extract diameter
		if insert_name == "A" || insert_name == "D"
			diameter = 1.2 # mm
		elseif insert_name == "B" || insert_name == "E"
			diameter = 3.0 # mm
		elseif insert_name == "C" || insert_name == "F"
			diameter = 5.0 # mm
		end

		# Inserts
		dcm_heart = dcm_arr .* heart_mask
		insert_threshold = 500
		centers_a, centers_b = get_insert_centers(dcm_heart, insert_threshold)
		cylinder_rad = diameter * 2.0
		cylinder = create_cylinder(dcm_heart, centers_a, centers_b, cylinder_rad, -25)
		background_rad = cylinder_rad + 6
		_background_ring = create_cylinder(dcm_heart, centers_a, centers_b, background_rad, -25)
		background_ring = Bool.(_background_ring .- cylinder)
	
		binary_calibration = falses(size(dcm_heart))
		binary_calibration[centers_a...] = true
		binary_calibration = dilate(binary_calibration)
		cylinder_clean = remove_outliers(dcm_heart[cylinder])
		background_clean = remove_outliers(dcm_heart[background_ring])
		
		# Offset Measurements
		off_num = 30
		offa1 = (centers_a[1] + off_num, centers_a[2] + off_num, centers_a[3])
		offb1 = (centers_b[1] + off_num, centers_b[2] + off_num, centers_b[3])
		offa2 = (centers_a[1] - off_num, centers_a[2] - off_num, centers_a[3])
		offb2 = (centers_b[1] - off_num, centers_b[2] - off_num, centers_b[3])
		offa3 = (centers_a[1] + off_num, centers_a[2] - off_num, centers_a[3])
		offb3 = (centers_b[1] + off_num, centers_b[2] - off_num, centers_b[3])
		
		offset_cylinder1 = create_cylinder(dcm_heart, offa1, offb1, cylinder_rad, -25)
		offset_cylinder2 = create_cylinder(dcm_heart, offa2, offb2, cylinder_rad, -25)
		offset_cylinder3 = create_cylinder(dcm_heart, offa3, offb3, cylinder_rad, -25)
		
		offset_background_ring1 = Bool.(create_cylinder(dcm_heart, offa1, offb1, background_rad, -25) .- offset_cylinder1)
		offset_background_ring2 = Bool.(create_cylinder(dcm_heart, offa2, offb2, background_rad, -25) .- offset_cylinder2)
		offset_background_ring3 = Bool.(create_cylinder(dcm_heart, offa3, offb3, background_rad, -25) .- offset_cylinder3)
		
		offset_cylinder1_clean = remove_outliers(dcm_heart[offset_cylinder1])
		offset_cylinder2_clean = remove_outliers(dcm_heart[offset_cylinder2])
		offset_cylinder3_clean = remove_outliers(dcm_heart[offset_cylinder3])
		
		offset_background_ring1_clean = remove_outliers(dcm_heart[offset_background_ring1])
		offset_background_ring2_clean = remove_outliers(dcm_heart[offset_background_ring2])
		offset_background_ring3_clean = remove_outliers(dcm_heart[offset_background_ring3])
		
		# Ground truth mass
		num_inserts = 3
		gt_length = 7 # mm
		gt_volume = π * (diameter/2)^2 * gt_length * num_inserts # mm^3
		gt_mass = gt_density * gt_volume
	
		# Volume fraction mass
		## Extract "calibration" from the endpoint inserts (ρ: 400 mg/cc)
		hu_calcium_400 = mean(dcm_heart[binary_calibration])
		ρ_calcium_400 = 0.400 # mg/mm^3

		## Calculate
		voxel_size = pixel_size[1] * pixel_size[2] * pixel_size[3]
		hu_heart_tissue_bkg = mean(background_clean)
		hu_heart_tissue_bkg_offset1 = mean(offset_background_ring1_clean)
		hu_heart_tissue_bkg_offset2 = mean(offset_background_ring2_clean)
		hu_heart_tissue_bkg_offset3 = mean(offset_background_ring3_clean)
		
		vf_mass = score(cylinder_clean, hu_calcium_400, hu_heart_tissue_bkg, voxel_size, ρ_calcium_400, VolumeFraction())
		vf_mass_offset1 = score(offset_cylinder1_clean, hu_calcium_400, hu_heart_tissue_bkg_offset1, voxel_size, ρ_calcium_400, VolumeFraction())
		vf_mass_offset2 = score(offset_cylinder2_clean, hu_calcium_400, hu_heart_tissue_bkg_offset2, voxel_size, ρ_calcium_400, VolumeFraction())
		vf_mass_offset3 = score(offset_cylinder3_clean, hu_calcium_400, hu_heart_tissue_bkg_offset3, voxel_size, ρ_calcium_400, VolumeFraction())
		
		vf_mass_bkg_mean = mean([vf_mass_offset1, vf_mass_offset2, vf_mass_offset3])
		vf_mass_bkg_std = std([vf_mass_offset1, vf_mass_offset2, vf_mass_offset3])
	
		# Agatston
		mass_cal_factor = ρ_calcium_400 / hu_calcium_400
		agatston_agatston, agatston_volume, agatston_mass = score(cylinder_clean, pixel_size, mass_cal_factor, Agatston(); kV=kV)
		
		_, _, agatston_mass_offset1 = score(offset_cylinder1_clean, pixel_size, mass_cal_factor, Agatston(); kV=kV)
		_, _, agatston_mass_offset2 = score(offset_cylinder2_clean, pixel_size, mass_cal_factor, Agatston(); kV=kV)
		_, _, agatston_mass_offset3 = score(offset_cylinder3_clean, pixel_size, mass_cal_factor, Agatston(); kV=kV)
		
		agatston_mass_bkg_mean = mean([agatston_mass_offset1, agatston_mass_offset2, agatston_mass_offset3])
		agatston_mass_bkg_std = std([agatston_mass_offset1, agatston_mass_offset2, agatston_mass_offset3])

		@info """
		Ground Truth Mass: $(gt_mass) \n
		Volume Fraction Mass: $(vf_mass) \n
		Volume Fraction Background Mass Mean + Std: $((vf_mass_bkg_mean, vf_mass_bkg_std)) \n
		Agatston Mass: $(agatston_mass) \n
		Agatston Background Mass Mean + Std: $((agatston_mass_bkg_mean, agatston_mass_bkg_std))
		"""
	
		# Push results to DataFrame
		push!(results_df, [insert_name, beats_per_minute, kV, mA, ctdi, gt_mass, vf_mass, vf_mass_bkg_mean, vf_mass_bkg_std, agatston_mass, agatston_mass_bkg_mean, agatston_mass_bkg_std])
	end

	output_filename = joinpath(pwd(),"data","$(insert_name)_$(beats_per_minute).csv")
	write(output_filename, results_df)
end

# ╔═╡ 4951072e-5546-4825-a12c-bb917df5995c
results_df

# ╔═╡ 00000000-0000-0000-0000-000000000001
PLUTO_PROJECT_TOML_CONTENTS = """
[deps]
CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0"
CalciumScoring = "9c0cb1da-21b1-4615-967b-153e03110a28"
DICOM = "a26e6606-dd52-5f6a-a97f-4f611373d757"
DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
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JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
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OrderedCollections = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
PlutoUI = "7f904dfe-b85e-4ff6-b463-dae2292396a8"
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Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"

[compat]
CSV = "~0.10.14"
CairoMakie = "~0.12.2"
CalciumScoring = "~0.4.0"
DICOM = "~0.11.0"
DataFrames = "~1.6.1"
DataInterpolations = "~5.2.0"
HTTP = "~1.10.8"
ImageCore = "~0.10.2"
ImageMorphology = "~0.4.5"
IterTools = "~1.10.0"
JSON = "~0.21.4"
OrderedCollections = "~1.6.3"
PlutoUI = "~0.7.59"
StaticArrays = "~1.9.4"
StatsBase = "~0.34.3"
"""

# ╔═╡ 00000000-0000-0000-0000-000000000002
PLUTO_MANIFEST_TOML_CONTENTS = """
# This file is machine-generated - editing it directly is not advised

julia_version = "1.10.4"
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[[deps.AbstractFFTs]]
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version = "1.5.0"
weakdeps = ["ChainRulesCore", "Test"]

    [deps.AbstractFFTs.extensions]
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[[deps.AbstractPlutoDingetjes]]
deps = ["Pkg"]
git-tree-sha1 = "6e1d2a35f2f90a4bc7c2ed98079b2ba09c35b83a"
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[[deps.AbstractTrees]]
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    GPUArraysCore = "46192b85-c4d5-4398-a991-12ede77f4527"
    SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
    StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"

[[deps.SuiteSparse]]
deps = ["Libdl", "LinearAlgebra", "Serialization", "SparseArrays"]
uuid = "4607b0f0-06f3-5cda-b6b1-a6196a1729e9"

[[deps.SuiteSparse_jll]]
deps = ["Artifacts", "Libdl", "libblastrampoline_jll"]
uuid = "bea87d4a-7f5b-5778-9afe-8cc45184846c"
version = "7.2.1+1"

[[deps.TOML]]
deps = ["Dates"]
uuid = "fa267f1f-6049-4f14-aa54-33bafae1ed76"
version = "1.0.3"

[[deps.TableTraits]]
deps = ["IteratorInterfaceExtensions"]
git-tree-sha1 = "c06b2f539df1c6efa794486abfb6ed2022561a39"
uuid = "3783bdb8-4a98-5b6b-af9a-565f29a5fe9c"
version = "1.0.1"

[[deps.Tables]]
deps = ["DataAPI", "DataValueInterfaces", "IteratorInterfaceExtensions", "LinearAlgebra", "OrderedCollections", "TableTraits"]
git-tree-sha1 = "cb76cf677714c095e535e3501ac7954732aeea2d"
uuid = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
version = "1.11.1"

[[deps.Tar]]
deps = ["ArgTools", "SHA"]
uuid = "a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"
version = "1.10.0"

[[deps.TensorCore]]
deps = ["LinearAlgebra"]
git-tree-sha1 = "1feb45f88d133a655e001435632f019a9a1bcdb6"
uuid = "62fd8b95-f654-4bbd-a8a5-9c27f68ccd50"
version = "0.1.1"

[[deps.Test]]
deps = ["InteractiveUtils", "Logging", "Random", "Serialization"]
uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

[[deps.ThreadingUtilities]]
deps = ["ManualMemory"]
git-tree-sha1 = "eda08f7e9818eb53661b3deb74e3159460dfbc27"
uuid = "8290d209-cae3-49c0-8002-c8c24d57dab5"
version = "0.5.2"

[[deps.TiffImages]]
deps = ["ColorTypes", "DataStructures", "DocStringExtensions", "FileIO", "FixedPointNumbers", "IndirectArrays", "Inflate", "Mmap", "OffsetArrays", "PkgVersion", "ProgressMeter", "SIMD", "UUIDs"]
git-tree-sha1 = "bc7fd5c91041f44636b2c134041f7e5263ce58ae"
uuid = "731e570b-9d59-4bfa-96dc-6df516fadf69"
version = "0.10.0"

[[deps.TiledIteration]]
deps = ["OffsetArrays", "StaticArrayInterface"]
git-tree-sha1 = "1176cc31e867217b06928e2f140c90bd1bc88283"
uuid = "06e1c1a7-607b-532d-9fad-de7d9aa2abac"
version = "0.5.0"

[[deps.TranscodingStreams]]
git-tree-sha1 = "a947ea21087caba0a798c5e494d0bb78e3a1a3a0"
uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa"
version = "0.10.9"
weakdeps = ["Random", "Test"]

    [deps.TranscodingStreams.extensions]
    TestExt = ["Test", "Random"]

[[deps.Tricks]]
git-tree-sha1 = "eae1bb484cd63b36999ee58be2de6c178105112f"
uuid = "410a4b4d-49e4-4fbc-ab6d-cb71b17b3775"
version = "0.1.8"

[[deps.TriplotBase]]
git-tree-sha1 = "4d4ed7f294cda19382ff7de4c137d24d16adc89b"
uuid = "981d1d27-644d-49a2-9326-4793e63143c3"
version = "0.1.0"

[[deps.URIs]]
git-tree-sha1 = "67db6cc7b3821e19ebe75791a9dd19c9b1188f2b"
uuid = "5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4"
version = "1.5.1"

[[deps.UUIDs]]
deps = ["Random", "SHA"]
uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"

[[deps.UnPack]]
git-tree-sha1 = "387c1f73762231e86e0c9c5443ce3b4a0a9a0c2b"
uuid = "3a884ed6-31ef-47d7-9d2a-63182c4928ed"
version = "1.0.2"

[[deps.Unicode]]
uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"

[[deps.UnicodeFun]]
deps = ["REPL"]
git-tree-sha1 = "53915e50200959667e78a92a418594b428dffddf"
uuid = "1cfade01-22cf-5700-b092-accc4b62d6e1"
version = "0.4.1"

[[deps.Unitful]]
deps = ["Dates", "LinearAlgebra", "Random"]
git-tree-sha1 = "dd260903fdabea27d9b6021689b3cd5401a57748"
uuid = "1986cc42-f94f-5a68-af5c-568840ba703d"
version = "1.20.0"

    [deps.Unitful.extensions]
    ConstructionBaseUnitfulExt = "ConstructionBase"
    InverseFunctionsUnitfulExt = "InverseFunctions"

    [deps.Unitful.weakdeps]
    ConstructionBase = "187b0558-2788-49d3-abe0-74a17ed4e7c9"
    InverseFunctions = "3587e190-3f89-42d0-90ee-14403ec27112"

[[deps.VectorizationBase]]
deps = ["ArrayInterface", "CPUSummary", "HostCPUFeatures", "IfElse", "LayoutPointers", "Libdl", "LinearAlgebra", "SIMDTypes", "Static", "StaticArrayInterface"]
git-tree-sha1 = "e863582a41c5731f51fd050563ae91eb33cf09be"
uuid = "3d5dd08c-fd9d-11e8-17fa-ed2836048c2f"
version = "0.21.68"

[[deps.WeakRefStrings]]
deps = ["DataAPI", "InlineStrings", "Parsers"]
git-tree-sha1 = "b1be2855ed9ed8eac54e5caff2afcdb442d52c23"
uuid = "ea10d353-3f73-51f8-a26c-33c1cb351aa5"
version = "1.4.2"

[[deps.WoodburyMatrices]]
deps = ["LinearAlgebra", "SparseArrays"]
git-tree-sha1 = "c1a7aa6219628fcd757dede0ca95e245c5cd9511"
uuid = "efce3f68-66dc-5838-9240-27a6d6f5f9b6"
version = "1.0.0"

[[deps.WorkerUtilities]]
git-tree-sha1 = "cd1659ba0d57b71a464a29e64dbc67cfe83d54e7"
uuid = "76eceee3-57b5-4d4a-8e66-0e911cebbf60"
version = "1.6.1"

[[deps.XML2_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Libiconv_jll", "Zlib_jll"]
git-tree-sha1 = "52ff2af32e591541550bd753c0da8b9bc92bb9d9"
uuid = "02c8fc9c-b97f-50b9-bbe4-9be30ff0a78a"
version = "2.12.7+0"

[[deps.XSLT_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Libgcrypt_jll", "Libgpg_error_jll", "Libiconv_jll", "Pkg", "XML2_jll", "Zlib_jll"]
git-tree-sha1 = "91844873c4085240b95e795f692c4cec4d805f8a"
uuid = "aed1982a-8fda-507f-9586-7b0439959a61"
version = "1.1.34+0"

[[deps.Xorg_libX11_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_libxcb_jll", "Xorg_xtrans_jll"]
git-tree-sha1 = "afead5aba5aa507ad5a3bf01f58f82c8d1403495"
uuid = "4f6342f7-b3d2-589e-9d20-edeb45f2b2bc"
version = "1.8.6+0"

[[deps.Xorg_libXau_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl"]
git-tree-sha1 = "6035850dcc70518ca32f012e46015b9beeda49d8"
uuid = "0c0b7dd1-d40b-584c-a123-a41640f87eec"
version = "1.0.11+0"

[[deps.Xorg_libXdmcp_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl"]
git-tree-sha1 = "34d526d318358a859d7de23da945578e8e8727b7"
uuid = "a3789734-cfe1-5b06-b2d0-1dd0d9d62d05"
version = "1.1.4+0"

[[deps.Xorg_libXext_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_libX11_jll"]
git-tree-sha1 = "d2d1a5c49fae4ba39983f63de6afcbea47194e85"
uuid = "1082639a-0dae-5f34-9b06-72781eeb8cb3"
version = "1.3.6+0"

[[deps.Xorg_libXrender_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_libX11_jll"]
git-tree-sha1 = "47e45cd78224c53109495b3e324df0c37bb61fbe"
uuid = "ea2f1a96-1ddc-540d-b46f-429655e07cfa"
version = "0.9.11+0"

[[deps.Xorg_libpthread_stubs_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl"]
git-tree-sha1 = "8fdda4c692503d44d04a0603d9ac0982054635f9"
uuid = "14d82f49-176c-5ed1-bb49-ad3f5cbd8c74"
version = "0.1.1+0"

[[deps.Xorg_libxcb_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "XSLT_jll", "Xorg_libXau_jll", "Xorg_libXdmcp_jll", "Xorg_libpthread_stubs_jll"]
git-tree-sha1 = "b4bfde5d5b652e22b9c790ad00af08b6d042b97d"
uuid = "c7cfdc94-dc32-55de-ac96-5a1b8d977c5b"
version = "1.15.0+0"

[[deps.Xorg_xtrans_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl"]
git-tree-sha1 = "e92a1a012a10506618f10b7047e478403a046c77"
uuid = "c5fb5394-a638-5e4d-96e5-b29de1b5cf10"
version = "1.5.0+0"

[[deps.Zlib_jll]]
deps = ["Libdl"]
uuid = "83775a58-1f1d-513f-b197-d71354ab007a"
version = "1.2.13+1"

[[deps.isoband_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "51b5eeb3f98367157a7a12a1fb0aa5328946c03c"
uuid = "9a68df92-36a6-505f-a73e-abb412b6bfb4"
version = "0.2.3+0"

[[deps.libaom_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl"]
git-tree-sha1 = "1827acba325fdcdf1d2647fc8d5301dd9ba43a9d"
uuid = "a4ae2306-e953-59d6-aa16-d00cac43593b"
version = "3.9.0+0"

[[deps.libass_jll]]
deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "HarfBuzz_jll", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"]
git-tree-sha1 = "5982a94fcba20f02f42ace44b9894ee2b140fe47"
uuid = "0ac62f75-1d6f-5e53-bd7c-93b484bb37c0"
version = "0.15.1+0"

[[deps.libblastrampoline_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "8e850b90-86db-534c-a0d3-1478176c7d93"
version = "5.8.0+1"

[[deps.libfdk_aac_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "daacc84a041563f965be61859a36e17c4e4fcd55"
uuid = "f638f0a6-7fb0-5443-88ba-1cc74229b280"
version = "2.0.2+0"

[[deps.libpng_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Zlib_jll"]
git-tree-sha1 = "d7015d2e18a5fd9a4f47de711837e980519781a4"
uuid = "b53b4c65-9356-5827-b1ea-8c7a1a84506f"
version = "1.6.43+1"

[[deps.libsixel_jll]]
deps = ["Artifacts", "JLLWrappers", "JpegTurbo_jll", "Libdl", "Pkg", "libpng_jll"]
git-tree-sha1 = "d4f63314c8aa1e48cd22aa0c17ed76cd1ae48c3c"
uuid = "075b6546-f08a-558a-be8f-8157d0f608a5"
version = "1.10.3+0"

[[deps.libvorbis_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Ogg_jll", "Pkg"]
git-tree-sha1 = "b910cb81ef3fe6e78bf6acee440bda86fd6ae00c"
uuid = "f27f6e37-5d2b-51aa-960f-b287f2bc3b7a"
version = "1.3.7+1"

[[deps.nghttp2_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"
version = "1.52.0+1"

[[deps.oneTBB_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl"]
git-tree-sha1 = "7d0ea0f4895ef2f5cb83645fa689e52cb55cf493"
uuid = "1317d2d5-d96f-522e-a858-c73665f53c3e"
version = "2021.12.0+0"

[[deps.p7zip_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0"
version = "17.4.0+2"

[[deps.x264_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "4fea590b89e6ec504593146bf8b988b2c00922b2"
uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a"
version = "2021.5.5+0"

[[deps.x265_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "ee567a171cce03570d77ad3a43e90218e38937a9"
uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76"
version = "3.5.0+0"
"""

# ╔═╡ Cell order:
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