diff --git a/figures/AP_n20_area.pdf b/figures/AP_n20_area.pdf
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diff --git a/figures/Manuscript-Figure AP n20.ipynb b/figures/Manuscript-Figure AP n20.ipynb
new file mode 100644
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+++ b/figures/Manuscript-Figure AP n20.ipynb	
@@ -0,0 +1,334 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "plt.rc('text', usetex=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Read Noise2Seg Results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_measure(pre, exp, run=1, fraction=0.5, measure='SEG', score_type='validation_'):\n",
+    "    with open('/home/tibuch/Noise2Seg/experiments/{}_{}_run{}/fraction_{}/{}scores.csv'.format(pre, exp, run, fraction, score_type)) as f:\n",
+    "        line = f.readline()\n",
+    "        while line:\n",
+    "            line = line.strip().split(',')\n",
+    "            if line[0] == measure:\n",
+    "                return float(line[1])\n",
+    "            line = f.readline()\n",
+    "    return None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def read_Noise2Seg_results(pre, exp, measure='SEG', runs=[1,2,3,4,5], \n",
+    "                           fractions=[0.25, 0.5, 1.0, 2.0, 4.0, 8.0, 16.0, 32.0, 64.0, 100.0], score_type='validation_'):\n",
+    "    \n",
+    "    stats = []\n",
+    "    \n",
+    "    for frac in fractions:\n",
+    "        scores = []\n",
+    "        for r in runs:\n",
+    "            scores.append(get_measure(pre, exp, run=r, fraction=frac, measure=measure, score_type=score_type))\n",
+    "        \n",
+    "        scores = np.array(scores)\n",
+    "        stats.append([frac, np.mean(scores), np.std(scores)/np.sqrt(scores.shape[0])])\n",
+    "    \n",
+    "    return np.array(stats)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fraction to #Images"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def fraction_to_abs(fracs, max_num_imgs=3800):\n",
+    "    return np.round(max_num_imgs*fracs/100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "304.0"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fraction_to_abs(8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# DSB2018 n20: SEG scores on validation data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "joint_alpha0_1_dsb_n20 = read_Noise2Seg_results('alpha0.1', 'dsb_n20', measure='AP', runs=[1,2,3,4,5], \n",
+    "                           fractions=[0.25, 0.5, 1.0, 2.0, 4.0, 8.0, 16.0, 32.0, 64.0, 100.0])\n",
+    "\n",
+    "joint_alpha0_2_dsb_n20 = read_Noise2Seg_results('alpha0.2', 'dsb_n20', measure='AP', runs=[1,2,3,4,5], \n",
+    "                           fractions=[0.25, 0.5, 1.0, 2.0, 4.0, 8.0, 16.0, 32.0, 64.0, 100.0])\n",
+    "\n",
+    "joint_alpha0_3_dsb_n20 = read_Noise2Seg_results('alpha0.3', 'dsb_n20', measure='AP', runs=[1,2,3,4,5], \n",
+    "                           fractions=[0.25, 0.5, 1.0, 2.0, 4.0, 8.0, 16.0, 32.0, 64.0, 100.0])\n",
+    "\n",
+    "joint_alpha0_4_dsb_n20 = read_Noise2Seg_results('alpha0.4', 'dsb_n20', measure='AP', runs=[1,2,3,4,5], \n",
+    "                           fractions=[0.25, 0.5, 1.0, 2.0, 4.0, 8.0, 16.0, 32.0, 64.0, 100.0])\n",
+    "\n",
+    "joint_alpha0_5_dsb_n20 = read_Noise2Seg_results('alpha0.5', 'dsb_n20', measure='AP', runs=[1,2,3,4,5], \n",
+    "                           fractions=[0.25, 0.5, 1.0, 2.0, 4.0, 8.0, 16.0, 32.0, 64.0, 100.0])\n",
+    "\n",
+    "joint_alpha0_6_dsb_n20 = read_Noise2Seg_results('alpha0.6', 'dsb_n20', measure='AP', runs=[1,2,3,4,5], \n",
+    "                           fractions=[0.25, 0.5, 1.0, 2.0, 4.0, 8.0, 16.0, 32.0, 64.0, 100.0])\n",
+    "\n",
+    "joint_alpha0_7_dsb_n20 = read_Noise2Seg_results('alpha0.7', 'dsb_n20', measure='AP', runs=[1,2,3,4,5], \n",
+    "                           fractions=[0.25, 0.5, 1.0, 2.0, 4.0, 8.0, 16.0, 32.0, 64.0, 100.0])\n",
+    "\n",
+    "joint_alpha0_8_dsb_n20 = read_Noise2Seg_results('alpha0.8', 'dsb_n20', measure='AP', runs=[1,2,3,4,5], \n",
+    "                           fractions=[0.25, 0.5, 1.0, 2.0, 4.0, 8.0, 16.0, 32.0, 64.0, 100.0])\n",
+    "\n",
+    "joint_alpha0_9_dsb_n20 = read_Noise2Seg_results('alpha0.9', 'dsb_n20', measure='AP', runs=[1,2,3,4,5], \n",
+    "                           fractions=[0.25, 0.5, 1.0, 2.0, 4.0, 8.0, 16.0, 32.0, 64.0, 100.0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_best_alphas_for_fractions(exps, fractions=[0.25, 0.5, 1.0, 2.0, 4.0, 8.0, 16.0, 32.0, 64.0, 100.0],\n",
+    "                                    alpha_strs = ['0.1', '0.2', '0.3', '0.4', '0.5', '0.6', '0.7', '0.8', '0.9']):\n",
+    "    scores = []\n",
+    "    sems = []\n",
+    "    alphas = []\n",
+    "\n",
+    "    for i in fractions:\n",
+    "        best_score = 0.0\n",
+    "        best_sem = 0.0\n",
+    "        best_alpha = ''\n",
+    "        for num, exp in enumerate(exps):\n",
+    "\n",
+    "            for j in range (exp.shape[0]):\n",
+    "                if(exp[j][0]==i):\n",
+    "                    score = exp[j][1]\n",
+    "                    corresponding_sem = exp[j][2]\n",
+    "                    corresponding_alpha = alpha_strs[num]\n",
+    "                    break\n",
+    "            if(score > best_score):\n",
+    "                best_score = score\n",
+    "                best_sem = corresponding_sem\n",
+    "                best_alpha = corresponding_alpha\n",
+    "\n",
+    "        scores.append(best_score)\n",
+    "        sems.append(best_sem)\n",
+    "        alphas.append(best_alpha)\n",
+    "        \n",
+    "    return np.array(scores), np.array(sems), alphas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "fractions = np.array([0.25, 0.5, 1.0, 2.0, 4.0, 8.0, 16.0, 32.0, 64.0, 100.0])\n",
+    "best_scores, best_sems, best_alphas = get_best_alphas_for_fractions(exps=[joint_alpha0_1_dsb_n20, joint_alpha0_2_dsb_n20, joint_alpha0_3_dsb_n20, joint_alpha0_4_dsb_n20, joint_alpha0_5_dsb_n20, joint_alpha0_6_dsb_n20, joint_alpha0_7_dsb_n20, joint_alpha0_8_dsb_n20, joint_alpha0_9_dsb_n20],\n",
+    "                                                                  fractions=fractions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "baseline_dsb_n20 = read_Noise2Seg_results('fin', 'dsb_n20', measure='AP', runs=[1,2,3,4,5], \n",
+    "                           fractions=[0.25, 0.5, 1.0, 2.0, 4.0, 8.0, 16.0, 32.0, 64.0, 100.0], score_type = '')\n",
+    "\n",
+    "sequential_dsb_n20 = read_Noise2Seg_results('finSeq', 'dsb_n20', measure='AP', runs=[1,2,3,4,5], \n",
+    "                           fractions=[0.25, 0.5, 1.0, 2.0, 4.0, 8.0, 16.0, 32.0, 64.0, 100.0], score_type = '')\n",
+    "\n",
+    "best_joint_dsb_n20_scores = []\n",
+    "best_joint_dsb_n20_sems = []\n",
+    "\n",
+    "for i in range(len(best_alphas)):\n",
+    "    t = read_Noise2Seg_results('alpha'+best_alphas[i], 'dsb_n20', measure='AP', runs=[1,2,3,4,5], \n",
+    "                           fractions=[fractions[i]], score_type = '')\n",
+    "    best_joint_dsb_n20_scores.append(t[0,1])\n",
+    "    best_joint_dsb_n20_sems.append(t[0,2])\n",
+    "    \n",
+    "best_joint_dsb_n20_scores = np.array(best_joint_dsb_n20_scores)\n",
+    "best_joint_dsb_n20_sems = np.array(best_joint_dsb_n20_sems)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_joint_alpha0_5_dsb_n20 = read_Noise2Seg_results('alpha0.5', 'dsb_n20', measure='AP', runs=[1,2,3,4,5], \n",
+    "                           fractions=[0.25, 0.5, 1.0, 2.0, 4.0, 8.0, 16.0, 32.0, 64.0, 100.0], score_type='')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.rc('font', family = 'serif', size = 20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def cm2inch(*tupl, scale=3):\n",
+    "    inch = 2.54\n",
+    "    if isinstance(tupl[0], tuple):\n",
+    "        return tuple(scale * i/inch for i in tupl[0])\n",
+    "    else:\n",
+    "        return tuple(scale * i/inch for i in tupl)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 518.74x340.157 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=cm2inch(12.2/2,4)) # 12.2cm is the text-widht of the MICCAI template\n",
+    "\n",
+    "plt.plot(fraction_to_abs(baseline_dsb_n20[:, 0], max_num_imgs = 3800), \n",
+    "         baseline_dsb_n20[:, 1],\n",
+    "         color = 'red', alpha = 0.9)\n",
+    "plt.fill_between(fraction_to_abs(baseline_dsb_n20[:, 0], max_num_imgs = 3800), \n",
+    "                 y1 = baseline_dsb_n20[:, 1] + baseline_dsb_n20[:, 2], \n",
+    "                 y2 = baseline_dsb_n20[:, 1] - baseline_dsb_n20[:, 2], \n",
+    "                 color = 'red', alpha = 0.25, label = 'Baseline')\n",
+    "\n",
+    "plt.plot(fraction_to_abs(sequential_dsb_n20[:, 0], max_num_imgs = 3800), \n",
+    "         sequential_dsb_n20[:, 1],\n",
+    "         color = 'blue', alpha = 0.9)\n",
+    "plt.fill_between(fraction_to_abs(sequential_dsb_n20[:, 0], max_num_imgs = 3800), \n",
+    "                 y1 = sequential_dsb_n20[:, 1] + sequential_dsb_n20[:, 2], \n",
+    "                 y2 = sequential_dsb_n20[:, 1] - sequential_dsb_n20[:, 2], \n",
+    "                 color = 'blue', alpha = 0.2, label = 'Baseline Sequential')\n",
+    "\n",
+    "plt.plot(fraction_to_abs(fractions, max_num_imgs = 3800), \n",
+    "         best_joint_dsb_n20_scores,\n",
+    "         color = 'orange', alpha = 0.9)\n",
+    "plt.fill_between(fraction_to_abs(fractions, max_num_imgs = 3800), \n",
+    "                 y1 = best_joint_dsb_n20_scores + best_joint_dsb_n20_sems, \n",
+    "                 y2 = best_joint_dsb_n20_scores - best_joint_dsb_n20_sems, \n",
+    "                 color = 'orange', alpha = 0.15, label = r'\\textbf{Joint-Loss (Ours)}')\n",
+    "\n",
+    "plt.semilogx()\n",
+    "# plt.legend(loc = 'lower right')\n",
+    "\n",
+    "plt.ylabel(r'\\textbf{AP}')\n",
+    "plt.xlabel(r'\\textbf{Number of Images ($P_1$ to $P_{10}$)}')\n",
+    "\n",
+    "plt.grid(axis='y')\n",
+    "\n",
+    "plt.xticks(ticks=fraction_to_abs(fractions, max_num_imgs = 3800), \n",
+    "           labels=fraction_to_abs(fractions, max_num_imgs = 3800).astype(np.int),\n",
+    "           rotation=45)\n",
+    "plt.minorticks_off()\n",
+    "\n",
+    "plt.yticks(rotation=45)\n",
+    "\n",
+    "plt.xlim([8.5, 4500])\n",
+    "\n",
+    "plt.tight_layout();\n",
+    "\n",
+    "plt.savefig('AP_n20_area.pdf', pad_inches=0.0);"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/figures/Manuscript-Figure SEG n20.ipynb b/figures/Manuscript-Figure SEG n20.ipynb
new file mode 100644
index 0000000..cee4558
--- /dev/null
+++ b/figures/Manuscript-Figure SEG n20.ipynb	
@@ -0,0 +1,334 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "plt.rc('text', usetex=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Read Noise2Seg Results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_measure(pre, exp, run=1, fraction=0.5, measure='SEG', score_type='validation_'):\n",
+    "    with open('/home/tibuch/Noise2Seg/experiments/{}_{}_run{}/fraction_{}/{}scores.csv'.format(pre, exp, run, fraction, score_type)) as f:\n",
+    "        line = f.readline()\n",
+    "        while line:\n",
+    "            line = line.strip().split(',')\n",
+    "            if line[0] == measure:\n",
+    "                return float(line[1])\n",
+    "            line = f.readline()\n",
+    "    return None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def read_Noise2Seg_results(pre, exp, measure='SEG', runs=[1,2,3,4,5], \n",
+    "                           fractions=[0.25, 0.5, 1.0, 2.0, 4.0, 8.0, 16.0, 32.0, 64.0, 100.0], score_type='validation_'):\n",
+    "    \n",
+    "    stats = []\n",
+    "    \n",
+    "    for frac in fractions:\n",
+    "        scores = []\n",
+    "        for r in runs:\n",
+    "            scores.append(get_measure(pre, exp, run=r, fraction=frac, measure=measure, score_type=score_type))\n",
+    "        \n",
+    "        scores = np.array(scores)\n",
+    "        stats.append([frac, np.mean(scores), np.std(scores)/np.sqrt(scores.shape[0])])\n",
+    "    \n",
+    "    return np.array(stats)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Fraction to #Images"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def fraction_to_abs(fracs, max_num_imgs=3800):\n",
+    "    return np.round(max_num_imgs*fracs/100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "304.0"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fraction_to_abs(8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# DSB2018 n20: SEG scores on validation data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "joint_alpha0_1_dsb_n20 = read_Noise2Seg_results('alpha0.1', 'dsb_n20', measure='SEG', runs=[1,2,3,4,5], \n",
+    "                           fractions=[0.25, 0.5, 1.0, 2.0, 4.0, 8.0, 16.0, 32.0, 64.0, 100.0])\n",
+    "\n",
+    "joint_alpha0_2_dsb_n20 = read_Noise2Seg_results('alpha0.2', 'dsb_n20', measure='SEG', runs=[1,2,3,4,5], \n",
+    "                           fractions=[0.25, 0.5, 1.0, 2.0, 4.0, 8.0, 16.0, 32.0, 64.0, 100.0])\n",
+    "\n",
+    "joint_alpha0_3_dsb_n20 = read_Noise2Seg_results('alpha0.3', 'dsb_n20', measure='SEG', runs=[1,2,3,4,5], \n",
+    "                           fractions=[0.25, 0.5, 1.0, 2.0, 4.0, 8.0, 16.0, 32.0, 64.0, 100.0])\n",
+    "\n",
+    "joint_alpha0_4_dsb_n20 = read_Noise2Seg_results('alpha0.4', 'dsb_n20', measure='SEG', runs=[1,2,3,4,5], \n",
+    "                           fractions=[0.25, 0.5, 1.0, 2.0, 4.0, 8.0, 16.0, 32.0, 64.0, 100.0])\n",
+    "\n",
+    "joint_alpha0_5_dsb_n20 = read_Noise2Seg_results('alpha0.5', 'dsb_n20', measure='SEG', runs=[1,2,3,4,5], \n",
+    "                           fractions=[0.25, 0.5, 1.0, 2.0, 4.0, 8.0, 16.0, 32.0, 64.0, 100.0])\n",
+    "\n",
+    "joint_alpha0_6_dsb_n20 = read_Noise2Seg_results('alpha0.6', 'dsb_n20', measure='SEG', runs=[1,2,3,4,5], \n",
+    "                           fractions=[0.25, 0.5, 1.0, 2.0, 4.0, 8.0, 16.0, 32.0, 64.0, 100.0])\n",
+    "\n",
+    "joint_alpha0_7_dsb_n20 = read_Noise2Seg_results('alpha0.7', 'dsb_n20', measure='SEG', runs=[1,2,3,4,5], \n",
+    "                           fractions=[0.25, 0.5, 1.0, 2.0, 4.0, 8.0, 16.0, 32.0, 64.0, 100.0])\n",
+    "\n",
+    "joint_alpha0_8_dsb_n20 = read_Noise2Seg_results('alpha0.8', 'dsb_n20', measure='SEG', runs=[1,2,3,4,5], \n",
+    "                           fractions=[0.25, 0.5, 1.0, 2.0, 4.0, 8.0, 16.0, 32.0, 64.0, 100.0])\n",
+    "\n",
+    "joint_alpha0_9_dsb_n20 = read_Noise2Seg_results('alpha0.9', 'dsb_n20', measure='SEG', runs=[1,2,3,4,5], \n",
+    "                           fractions=[0.25, 0.5, 1.0, 2.0, 4.0, 8.0, 16.0, 32.0, 64.0, 100.0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_best_alphas_for_fractions(exps, fractions=[0.25, 0.5, 1.0, 2.0, 4.0, 8.0, 16.0, 32.0, 64.0, 100.0],\n",
+    "                                    alpha_strs = ['0.1', '0.2', '0.3', '0.4', '0.5', '0.6', '0.7', '0.8', '0.9']):\n",
+    "    scores = []\n",
+    "    sems = []\n",
+    "    alphas = []\n",
+    "\n",
+    "    for i in fractions:\n",
+    "        best_score = 0.0\n",
+    "        best_sem = 0.0\n",
+    "        best_alpha = ''\n",
+    "        for num, exp in enumerate(exps):\n",
+    "\n",
+    "            for j in range (exp.shape[0]):\n",
+    "                if(exp[j][0]==i):\n",
+    "                    score = exp[j][1]\n",
+    "                    corresponding_sem = exp[j][2]\n",
+    "                    corresponding_alpha = alpha_strs[num]\n",
+    "                    break\n",
+    "            if(score > best_score):\n",
+    "                best_score = score\n",
+    "                best_sem = corresponding_sem\n",
+    "                best_alpha = corresponding_alpha\n",
+    "\n",
+    "        scores.append(best_score)\n",
+    "        sems.append(best_sem)\n",
+    "        alphas.append(best_alpha)\n",
+    "        \n",
+    "    return np.array(scores), np.array(sems), alphas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "fractions = np.array([0.25, 0.5, 1.0, 2.0, 4.0, 8.0, 16.0, 32.0, 64.0, 100.0])\n",
+    "best_scores, best_sems, best_alphas = get_best_alphas_for_fractions(exps=[joint_alpha0_1_dsb_n20, joint_alpha0_2_dsb_n20, joint_alpha0_3_dsb_n20, joint_alpha0_4_dsb_n20, joint_alpha0_5_dsb_n20, joint_alpha0_6_dsb_n20, joint_alpha0_7_dsb_n20, joint_alpha0_8_dsb_n20, joint_alpha0_9_dsb_n20],\n",
+    "                                                                  fractions=fractions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "baseline_dsb_n20 = read_Noise2Seg_results('fin', 'dsb_n20', measure='SEG', runs=[1,2,3,4,5], \n",
+    "                           fractions=[0.25, 0.5, 1.0, 2.0, 4.0, 8.0, 16.0, 32.0, 64.0, 100.0], score_type = '')\n",
+    "\n",
+    "sequential_dsb_n20 = read_Noise2Seg_results('finSeq', 'dsb_n20', measure='SEG', runs=[1,2,3,4,5], \n",
+    "                           fractions=[0.25, 0.5, 1.0, 2.0, 4.0, 8.0, 16.0, 32.0, 64.0, 100.0], score_type = '')\n",
+    "\n",
+    "best_joint_dsb_n20_scores = []\n",
+    "best_joint_dsb_n20_sems = []\n",
+    "\n",
+    "for i in range(len(best_alphas)):\n",
+    "    t = read_Noise2Seg_results('alpha'+best_alphas[i], 'dsb_n20', measure='SEG', runs=[1,2,3,4,5], \n",
+    "                           fractions=[fractions[i]], score_type = '')\n",
+    "    best_joint_dsb_n20_scores.append(t[0,1])\n",
+    "    best_joint_dsb_n20_sems.append(t[0,2])\n",
+    "    \n",
+    "best_joint_dsb_n20_scores = np.array(best_joint_dsb_n20_scores)\n",
+    "best_joint_dsb_n20_sems = np.array(best_joint_dsb_n20_sems)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_joint_alpha0_5_dsb_n20 = read_Noise2Seg_results('alpha0.5', 'dsb_n20', measure='SEG', runs=[1,2,3,4,5], \n",
+    "                           fractions=[0.25, 0.5, 1.0, 2.0, 4.0, 8.0, 16.0, 32.0, 64.0, 100.0], score_type='')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def cm2inch(*tupl, scale=3):\n",
+    "    inch = 2.54\n",
+    "    if isinstance(tupl[0], tuple):\n",
+    "        return tuple(scale * i/inch for i in tupl[0])\n",
+    "    else:\n",
+    "        return tuple(scale * i/inch for i in tupl)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.rc('font', family = 'serif', size = 20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 518.74x340.157 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=cm2inch(12.2/2,4)) # 12.2cm is the text-widht of the MICCAI template\n",
+    "\n",
+    "plt.plot(fraction_to_abs(baseline_dsb_n20[:, 0], max_num_imgs = 3800), \n",
+    "         baseline_dsb_n20[:, 1],\n",
+    "         color = 'red', alpha = 0.9)\n",
+    "plt.fill_between(fraction_to_abs(baseline_dsb_n20[:, 0], max_num_imgs = 3800), \n",
+    "                 y1 = baseline_dsb_n20[:, 1] + baseline_dsb_n20[:, 2], \n",
+    "                 y2 = baseline_dsb_n20[:, 1] - baseline_dsb_n20[:, 2], \n",
+    "                 color = 'red', alpha = 0.25, label = 'Baseline')\n",
+    "\n",
+    "plt.plot(fraction_to_abs(sequential_dsb_n20[:, 0], max_num_imgs = 3800), \n",
+    "         sequential_dsb_n20[:, 1],\n",
+    "         color = 'blue', alpha = 0.9)\n",
+    "plt.fill_between(fraction_to_abs(sequential_dsb_n20[:, 0], max_num_imgs = 3800), \n",
+    "                 y1 = sequential_dsb_n20[:, 1] + sequential_dsb_n20[:, 2], \n",
+    "                 y2 = sequential_dsb_n20[:, 1] - sequential_dsb_n20[:, 2], \n",
+    "                 color = 'blue', alpha = 0.2, label = 'Baseline Sequential')\n",
+    "\n",
+    "plt.plot(fraction_to_abs(fractions, max_num_imgs = 3800), \n",
+    "         best_joint_dsb_n20_scores,\n",
+    "         color = 'orange', alpha = 0.9)\n",
+    "plt.fill_between(fraction_to_abs(fractions, max_num_imgs = 3800), \n",
+    "                 y1 = best_joint_dsb_n20_scores + best_joint_dsb_n20_sems, \n",
+    "                 y2 = best_joint_dsb_n20_scores - best_joint_dsb_n20_sems, \n",
+    "                 color = 'orange', alpha = 0.15, label = r'\\textbf{Joint-Loss (Ours)}')\n",
+    "\n",
+    "plt.semilogx()\n",
+    "plt.legend(loc = 'lower right')\n",
+    "\n",
+    "plt.ylabel(r'\\textbf{SEG}')\n",
+    "plt.xlabel(r'\\textbf{Number of Images ($P_1$ to $P_{10}$)}')\n",
+    "\n",
+    "plt.grid(axis='y')\n",
+    "\n",
+    "plt.xticks(ticks=fraction_to_abs(fractions, max_num_imgs = 3800), \n",
+    "           labels=fraction_to_abs(fractions, max_num_imgs = 3800).astype(np.int),\n",
+    "           rotation=45)\n",
+    "plt.minorticks_off()\n",
+    "\n",
+    "plt.yticks(rotation=45)\n",
+    "\n",
+    "plt.xlim([8.5, 4500])\n",
+    "\n",
+    "plt.tight_layout();\n",
+    "\n",
+    "plt.savefig('SEG_n20_area.pdf', pad_inches=0.0);"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/figures/SEG_n20_area.pdf b/figures/SEG_n20_area.pdf
new file mode 100644
index 0000000..b71d127
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